DD2528 Dependable Autonomous Systems 7.5 credits

Pålitliga autonoma system

Autonomous systems rely on artificial intelligence and machine learning to achieve autonomy. It is therefore a challenge to ensure dependability of an autonomous system and guarantee that the risks associated with the system are acceptable. The course will introduce modeling, verification and analysis techniques for achieving dependability of autonomous systems.

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Course information

Content and learning outcomes

Course contents *

Techniques to achieve dependability, safety analysis, derivation of dependability requirements from safety analysis, modelling and verification of safety requirements, safety assurance case, multi-agent systems, emergent behaviour, goal-oriented modelling and verification of safe and reliable multi-agent autonomous systems, evolutionary algorithms and learning algorithms for mission planning and navigation, safety of mission planning.

Intended learning outcomes *

After a passed course, the student should be able to

• represent data structures and their mutual dependencies as mathematical structures and formulate dependability properties by means of propositional logic,

• specify dynamic behaviour of autonomous systems and their properties,

• use risk assessment and safety analysis techniques to define dependability requirements,

• model and verify autonomous systems by means of automatic tools

in order to

· be able to work with autonomous safety critical systems in research and/or development,

  • be able to identify risks in connection with autonomous systems and use modelling, verification and security techniques to prevent them.

Course Disposition

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Literature and preparations

Specific prerequisites *

Completed courses equivalent:

SF1671 Mathematics, basic course with discrete mathematics
DD1337 Programming
DD1338 Algorithms and Data Structures
DD1350/DD1351 Logic for Computer Scientists
DD1393 Software Engineering

Recommended prerequisites

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Equipment

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Literature

Information about the course literature will be announced in the course memo.

Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale *

A, B, C, D, E, FX, F

Examination *

  • LAB1 - Laboratory work, 5.5 credits, Grading scale: A, B, C, D, E, FX, F
  • TEN1 - Written exam, 2.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

The examiner decides, in consultation with KTH's coordinator for disabilities (Funka), about possible adapted examination for students with documented, permanent disabilities. The examiner may permit other examination format for re-examination of individual students.

Opportunity to complete the requirements via supplementary examination

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Opportunity to raise an approved grade via renewed examination

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Examiner

Elena Troubitsyna

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web DD2528

Offered by

EECS/Computer Science

Main field of study *

Computer Science and Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex